This paper tackles the challenge of achieving a stable transition in tiltrotor aircraft, where control effectiveness varies significantly due to shifting flight regimes. We present a transition control framework that combines nonlinear dynamic inversion with online estimation of the control effectiveness matrix using recursive least squares. To design the transition control law, a trim analysis is first conducted to characterize feasible flight conditions, and a reference transition path is determined based on the resulting trim envelope. The control law is formulated to regulate the velocity along the direction of the tilting rotor and the angular rates about all three body axes. A quadratic programming-based control allocation scheme is employed to distribute control forces and moments, accounting for variations in actuator effectiveness and availability across flight modes. To facilitate reliable parameter identification without interfering with the nominal control allocation, a null-space input excitation strategy is incorporated into the allocation framework. Simulation results demonstrate that the presented approach enables stable forward and backward transition maneuvers, with real-time estimation of control effectiveness initialized from nominal values, without requiring full prior knowledge.

This work covers the design of a sliding mode control to stabilize the attitude of a flapping-wing micro aerial vehicle. The approach employs an auxiliary observer loop to avoid system excitation from unmodeled actuator dynamics, a common issue in sliding mode control applications. A proportional-integral observer is constituted in the auxiliary loop to minimize interactions with the actuator dynamics and to handle parametric uncertainties in the low bandwidth. Then, the observer-based sliding mode control is designed to track the attitude command with the reconstructed state variables from the observer loop. Furthermore, a barrier function-based adaptive gain strategy is utilized to modulate the control input according to the system's current state, ensuring efficient use of control effort. Flight experiments were conducted with a freely movable dummy mass attached to the bottom of the vehicle, simulating external disturbances. The proposed sliding mode control outperforms proportional-derivative (PD), classical, and super-twisting sliding mode controllers in both tracking performance and control efficiency, while mitigating self-excitation due to discontinuous input.

Operational reliability of multi-rotor Unmanned Aerial Vehicles (UAVs) is frequently compromised by the ambiguity between external wind disturbances and internal thrust faults. This paper proposes a physics-informed fault diagnosis (PIFDI) framework that explicitly decouples wind-induced effects from total observed disturbances. By integrating an Extended Kalman Filter (EKF) for real-time wind estimation and a Disturbance Observer (DOB) for total torque monitoring, the framework isolates a clean fault residual through physical coefficient mapping. High-fidelity 6-DOF simulations involving Dryden turbulence and non-stationary discrete gusts demonstrate a rapid detection latency of 0.18 s for a 20% thrust loss, maintaining near-zero false alarms even during peak gust periods. Furthermore, a 300-trial Monte Carlo simulation confirmed high fault isolation accuracy, demonstratingsuperior statistical robustness across varying wind intensities and randomized fault modes. The proposed physicsinformeddecoupling approach significantly enhances diagnostic resilience, providing a critical foundation for real-time fault-tolerant control in mission-critical UAV operations.

This study proposes a mission optimization framework for the datalink-enabled anti-ship cruise missile (ASCM). Mission control variables include target assignment, impact course, and simultaneous time-on-target (STOT) offsets. Mission performance is evaluated using a probabilistic layered defense survivability model with Monte Carlo–based estimation. We solve the resulting time-bounded mission replanning problem using particle swarm optimization (PSO) with a fast–fine fitness evaluation strategy and a repair-based procedure for operational constraints, including inter-missile collision avoidance. Simulation results show improved mission effectiveness and survivability compared with a densest-STOT baseline. This framework provides a methodological basis for practical dynamic mission control of ASCMs in network-centric warfare environments.

This paper proposes a quantitative strike assessment method for Network-Enabled Anti-ship Cruise Missiles (NE-ASCM). Based on an analysis of operational concepts and relevant technologies, it introduces a novel metric, the Bomb Hit Indicator (BHI), computed using limited information received via datalink and reflecting the constraints derived from these concepts and technologies. Using this metric, a practical evaluation framework is developed for real-world implementation. The proposed system supports re-engagement decision-making in networked combat environments, thereby enhancing operational effectiveness and improving combat sustainability.

Flapping-Wing Unmanned Aerial Vehicles (FWUAV) exhibit highly non-linear and time-periodic dynamics. Due to these characteristics, conventional modeling and stability analysis methods are often inadequate, and standard linear controllers typically result in redundant control inputs. This paper focuses on the FWUAV as a periodic system, encompassing the analysis of its periodic stability and the design of an efficient control framework based on simplified dynamics. First, a Multi-Phase Multiple-Shooting (MPMS) algorithm is employed to identify equilibrium limit cycle conditions, and the orbital stability of the periodic motion is verified using Floquet theory. To enhance the efficiency of the control design, a Linear Time-Periodic (LTP) system identification technique based on Fourier series is employed. This approach reformulates the complex non-linear aerodynamic and inertial effects into a time-varying linear model suitable for controller design. Based on the identified LTP model, an Event-Triggered Control (ETC) strategy is developed to suppress unnecessary input oscillations and significantly reduce actuator loads. Numerical simulations demonstrate that the proposed framework provides a robust and energy-efficient solution for the stable operation of bioinspired flapping-wing flight systems by addressing the inherent periodic effects of the vehicle.

An optimal transition control scheme is proposed for a lift+cruise vertical take-off and landing fixed-wing unmanned aerial vehicle. To maintain altitude during the UAV's acceleration from hover to cruise and deceleration from cruise to hover, pitch angle-speed corridors that satisfy the dynamic transition conditions are designed. The proposed control system is composed of trajectory optimization using the transition corridor as inequality constraints, and an inner-loop position and attitude controller design based on nonlinear dynamic inversion technique. Numerical simulation is performed to demonstrate the effectiveness of the proposed control scheme and its performance under wind disturbances. The mode-switching results highlight the importance of incorporating transition corridors into trajectory generation to ensure safe and smooth transitions.

Urban air mobility (UAM) has emerged as a potential solution to mitigate urban traffic congestion. However, severe turbulence in urban wind environments poses a significant safety issue for UAM operations. To ensure safe and reliable UAM operations, a UAM hazard prediction system is essential. This study proposes a UAM flight hazard index prediction system. To achieve this, first, UAM flight data were generated through the coupling of a UAM dynamics simulator with an actual urban wind environment. The urban wind environment was produced using the Weather Research and Forecasting-Large Eddy Simulation coupled model. By applying wingless type and lift&cruise type UAM dynamics simulators to these urban wind environments, a flight simulation database was constructed. For the assessment of the UAM flight hazard, a new hazard index, (v) over right arrow (dev), was derived from wind components that induce path deviations. Analyses confirmed that (v) over right arrow (dev) indicates wind-induced hazards while accounting for both wind magnitude and direction. Long Short-Term Memory networks were then trained using the flight simulation database to predict the hazard index. In particular, an initializer neural network was incorporated to enable predictions from arbitrary initial states. The resulting models demonstrated high accuracy for both types of UAM. Using these models, the hazard index in UAM corridors was evaluated. The results exhibited different trends in the hazard index under varying wind conditions. Under the headwind and tailwind conditions, the hazard index values were low for both types. In contrast, under crosswind conditions, the hazard index was high. The wind speed increasing with altitude was another factor contributing to the hazard index. Additionally, different hazard index values were observed between the two UAM types under the same wind conditions due to the different flight characteristics.

This paper investigates a robust Takagi–Sugeno (T–S) fuzzy controller for flapping-wing micro aerial vehicles (FWMAVs) subject to actuator saturation and external disturbances. To this end, we first construct a state-scheduled T–S fuzzy model to represent the longitudinal dynamics of FWMAVs, explicitly accounting for state-dependent nonlinearities. Based on this model, we propose a two-loop control architecture: an inner-loop T–S fuzzy controller that regulates the vertical position and pitch angle, and an outer-loop PD controller that achieves full position-tracking by generating a pitch reference from the desired longitudinal position. Then, we formulate the T–S fuzzy stabilization conditions as linear matrix inequalities (LMIs) that guarantee closed-loop stability. Specifically, by exploiting the mismatch between the current and subsequent fuzzy basis functions (FBFs), we introduce a relaxation method that incorporates additional slack variables into the stabilization conditions, thereby reducing conservatism. Finally, numerical comparisons and simulations for FWMAVs are presented to verify the reduced conservatism and effectiveness of the proposed method.

The fixed-wing UAV, which is launched from a canister for rapid mission deployment, has a tandem-wing configuration that ensures a large wing area even under constrained design conditions, considering storage requirements. Therefore, it is essential to develop a nonlinear simulation model that accurately reflects dynamic characteristics. In this study, the aerodynamic characteristics are analyzed using Computational Fluid Dynamics (CFD) and Vortex Lattice Method (VLM) methods, and the propulsion system was modeled by converting the thrust and torque coefficients for commercial propellers into advance ratio based equations. The proposed guidance and control approach for the nonlinear dynamic model was demonstrated using a hardware-in-the-loop simulation (HILS) environment.

The currently operational geostationary orbit satellites in Korea utilize seven thrusters for tasks such as attitude control, orbit maintenance, and wheel offloading missions. Three thrusters in charge of roll and pitch attitude control as well as northward orbit maintenance are positioned on the same side as the solar panels. This configuration leads to plume disturbance during thruster operation, as the exhaust interferes with the solar panels, potentially impairing thrust performance, particularly during northward orbit maintenance maneuvers. Thus, orbit maintenance commands must account for this disturbance. In the satellite design phase, accurately calculating plume disturbance values for northward orbit maintenance is essential. This study aims to validate the plume disturbance values during northward orbit maintenance by comparing them with actual satellite operational data. To accomplish this objective, simulations incorporating plume disturbance are executed to examine the attitude control characteristics. As a result, it is confined that the observations in the simulations correspond with the actual operational data of the satellite.

The urban air mobility (UAM) market has grown rapidly in recent years. While UAM is expected to be deployed within a few years, ensuring safety is essential for successful implementation. One of the factors related to UAM safety is the urban wind environment. Characterized by strong and unpredictable turbulence and building-induced winds due to complex urban topography, the urban wind environment poses significant risks to UAMs, which are lightweight and slow. This study analyzes the flight hazards of UAMs caused by urban wind environments. A realistic urban wind environment was simulated by combining the Weather Research & Forecasting model with large-eddy simulation. By integrating the simulated urban wind environment with two types of UAM simulators, a UAM flight database was constructed. Qualitative and quantitative analyses of the flight database identified hazardous wind speeds caused by crosswinds and vertical winds for the two UAM types. For the Gangnam area, hazardous regions were predicted based on wind speeds. The results revealed that the hazard from crosswind was influenced by shear layers, corner effects, and atmospheric boundary layer, while hazard from vertical wind was primarily occurred at wake regions.

Incremental nonlinear dynamic inversion (INDI) controllers effectively handle disturbances and model uncertainties using angular acceleration feedback. However, conventional INDI methods face challenges in quadrotor and hexacopter UAVs, such as the need for a pseudo-inverse matrix and the lack of clear guidelines for selecting baseline proportional control gains, which are crucial for stable flight. This paper proposes a torque-based INDI controller, eliminating the need for a pseudo-inverse matrix by estimating real-time torques. It also provides practical guidelines for selecting control gains based on the system’s mass moment of inertia, enabling stable initial flight tests. The proposed method is validated through flight tests on a slung-load system with disturbances, demonstrating its robustness and effectiveness.

This article proposes anapproach based on the adaptive robust extended Kalman filter (AREKF) suitable for estimating the state-of-charge (SoC) of small unmanned aerial vehicle (sUAV). The SoC of sUAV is a crucial factor directly affecting the remaining flying time (RFT). Existing methods for SoC estimation heavily rely on elaborate battery charge-discharge experiments conducted in complex environments, limiting their applicability to sUAV. This article combines the Shepherd battery model with AREKF to estimate the SoC of sUAV using a small amount of operational data. To verify the effectiveness of the proposed method, this article utilizes publicly available automotive data (Panasonic 18650PF Battery Data) and aviation data (NASA High-Intensity Radiated Field Battery Data). The adaptive extended Kalman filter (AEKF) serves as the control group for evaluating the performance of the SoC estimation. Ultimately, the data obtained from field flight tests are employed to evaluate the RFT predictions of AREKF and AEKF. The feasibility and performance of the proposed method are demonstrated through the offline test using numerical simulation. AREKF yields superior results with lower errors and variations in both SoC estimation and RFT prediction performance compared with AEKF.